{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How passing of time impacts datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import rc\n",
    "import pandas as pd\n",
    "\n",
    "# plot style\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_style({'font.family': 'Times New Roman'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv(\"data/metadata_merged.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/dataset_level.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.merge(df,metadata[['doi','publicationDate']],on='doi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "merged.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1211.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.393400e+04</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1813.500000</td>\n",
       "      <td>249.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.924366e+06</td>\n",
       "      <td>4.925000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.791667</td>\n",
       "      <td>19.230000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1062.670455</td>\n",
       "      <td>184.659091</td>\n",
       "      <td>14.454545</td>\n",
       "      <td>1.050298e+08</td>\n",
       "      <td>105.030114</td>\n",
       "      <td>13.852273</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>4.193182</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397727</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>23.070646</td>\n",
       "      <td>17.404545</td>\n",
       "      <td>6.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1055.540441</td>\n",
       "      <td>207.297794</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.147157e+07</td>\n",
       "      <td>91.471654</td>\n",
       "      <td>19.386029</td>\n",
       "      <td>0.573529</td>\n",
       "      <td>3.797794</td>\n",
       "      <td>0.025735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.345588</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.018382</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>19.833321</td>\n",
       "      <td>17.494779</td>\n",
       "      <td>6.676471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1009.588957</td>\n",
       "      <td>229.935583</td>\n",
       "      <td>16.733129</td>\n",
       "      <td>9.834318e+07</td>\n",
       "      <td>98.343098</td>\n",
       "      <td>18.598160</td>\n",
       "      <td>0.564417</td>\n",
       "      <td>4.395706</td>\n",
       "      <td>0.058282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322086</td>\n",
       "      <td>0.297546</td>\n",
       "      <td>0.021472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>25.344336</td>\n",
       "      <td>17.726840</td>\n",
       "      <td>6.662577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1062.564854</td>\n",
       "      <td>198.460251</td>\n",
       "      <td>16.351464</td>\n",
       "      <td>7.109928e+07</td>\n",
       "      <td>71.099351</td>\n",
       "      <td>14.238494</td>\n",
       "      <td>0.571130</td>\n",
       "      <td>3.317992</td>\n",
       "      <td>0.043933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.330544</td>\n",
       "      <td>0.341004</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.967414</td>\n",
       "      <td>17.784268</td>\n",
       "      <td>7.658996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1039.395480</td>\n",
       "      <td>283.659134</td>\n",
       "      <td>16.764595</td>\n",
       "      <td>1.105798e+08</td>\n",
       "      <td>110.580019</td>\n",
       "      <td>18.216573</td>\n",
       "      <td>0.591337</td>\n",
       "      <td>3.798493</td>\n",
       "      <td>0.073446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001883</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.306968</td>\n",
       "      <td>0.048964</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.330026</td>\n",
       "      <td>16.833465</td>\n",
       "      <td>8.438795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1069.451327</td>\n",
       "      <td>259.938053</td>\n",
       "      <td>21.557522</td>\n",
       "      <td>6.942743e+07</td>\n",
       "      <td>69.427345</td>\n",
       "      <td>17.672566</td>\n",
       "      <td>0.584071</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.044248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.314159</td>\n",
       "      <td>0.274336</td>\n",
       "      <td>0.039823</td>\n",
       "      <td>0.017699</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.169279</td>\n",
       "      <td>17.410752</td>\n",
       "      <td>8.384956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Unnamed: 0  comments_no  dependen_no    total_size  \\\n",
       "publicationDate                                                        \n",
       "2010             1211.000000    14.000000     1.000000  1.393400e+04   \n",
       "2014             1813.500000   249.500000     9.000000  4.924366e+06   \n",
       "2015             1062.670455   184.659091    14.454545  1.050298e+08   \n",
       "2016             1055.540441   207.297794    13.000000  9.147157e+07   \n",
       "2017             1009.588957   229.935583    16.733129  9.834318e+07   \n",
       "2018             1062.564854   198.460251    16.351464  7.109928e+07   \n",
       "2019             1039.395480   283.659134    16.764595  1.105798e+08   \n",
       "2020             1069.451327   259.938053    21.557522  6.942743e+07   \n",
       "\n",
       "                     sizeMB  files_count      docs    r_file      test  \\\n",
       "publicationDate                                                          \n",
       "2010               0.010000     2.000000  0.000000  1.000000  0.000000   \n",
       "2014               4.925000     7.000000  0.500000  4.500000  0.000000   \n",
       "2015             105.030114    13.852273  0.545455  4.193182  0.056818   \n",
       "2016              91.471654    19.386029  0.573529  3.797794  0.025735   \n",
       "2017              98.343098    18.598160  0.564417  4.395706  0.058282   \n",
       "2018              71.099351    14.238494  0.571130  3.317992  0.043933   \n",
       "2019             110.580019    18.216573  0.591337  3.798493  0.073446   \n",
       "2020              69.427345    17.672566  0.584071  4.000000  0.044248   \n",
       "\n",
       "                 test_lib  ...  wflow_lib  dockerfile     space  other_code  \\\n",
       "publicationDate            ...                                                \n",
       "2010                  0.0  ...        0.0    0.000000  0.000000    0.000000   \n",
       "2014                  0.0  ...        0.0    0.000000  0.000000    0.500000   \n",
       "2015                  0.0  ...        0.0    0.000000  0.397727    0.284091   \n",
       "2016                  0.0  ...        0.0    0.000000  0.345588    0.382353   \n",
       "2017                  0.0  ...        0.0    0.000000  0.322086    0.297546   \n",
       "2018                  0.0  ...        0.0    0.000000  0.330544    0.341004   \n",
       "2019                  0.0  ...        0.0    0.001883  0.288136    0.306968   \n",
       "2020                  0.0  ...        0.0    0.026549  0.314159    0.274336   \n",
       "\n",
       "                      rmd     rproj       rnw  comments_no_files  \\\n",
       "publicationDate                                                    \n",
       "2010             0.000000  0.000000  0.000000           7.000000   \n",
       "2014             0.000000  0.000000  0.000000          35.791667   \n",
       "2015             0.011364  0.000000  0.022727          23.070646   \n",
       "2016             0.018382  0.007353  0.007353          19.833321   \n",
       "2017             0.021472  0.000000  0.003067          25.344336   \n",
       "2018             0.020921  0.006276  0.000000          22.967414   \n",
       "2019             0.048964  0.011299  0.000000          29.330026   \n",
       "2020             0.039823  0.017699  0.000000          26.169279   \n",
       "\n",
       "                 avg_file_len  unique_libs_no  \n",
       "publicationDate                                \n",
       "2010                 8.000000        1.000000  \n",
       "2014                19.230000        3.500000  \n",
       "2015                17.404545        6.022727  \n",
       "2016                17.494779        6.676471  \n",
       "2017                17.726840        6.662577  \n",
       "2018                17.784268        7.658996  \n",
       "2019                16.833465        8.438795  \n",
       "2020                17.410752        8.384956  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.groupby('publicationDate').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged[merged.publicationDate >= 2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'doi', u'list_of_all', u'comments_no', u'dependen_no',\n",
       "       u'list_of_libs', u'total_size', u'sizeMB', u'files_count', u'docs',\n",
       "       u'r_file', u'test', u'test_lib', u'prov', u'wflow_lib', u'dockerfile',\n",
       "       u'space', u'other_code', u'rmd', u'rproj', u'rnw', u'comments_no_files',\n",
       "       u'avg_file_len', u'unique_libs_no', u'publicationDate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 4))\n",
    "\n",
    "ci_=80\n",
    "y_ = [\"sizeMB\", \"files_count\", \"r_file\"]\n",
    "y_y = [\"Size [MB]\", \"Count\", \"Count\"]\n",
    "y_title = [\"Dataset size\", \"Number of files\", \"Number of R code files\"]\n",
    "for i in range(0,3):\n",
    "    temp = sns.lineplot(data=merged, x=\"publicationDate\", y=y_[i], ci=ci_, ax=axes[0,i])\n",
    "    temp.set_xlabel(\"\")\n",
    "    temp.set_ylabel(y_y[i])\n",
    "    temp.set_title(y_title[i])\n",
    "\n",
    "y_ = [\"dependen_no\", \"unique_libs_no\", \"comments_no\"]\n",
    "y_y = [\"Count\", \"Count\", \"Count\"]\n",
    "y_title = [\"Number of libraries\", \"Number of unique libraries\", \"Number of code comments\"]\n",
    "for i in range(0,3):\n",
    "    temp = sns.lineplot(data=merged, x=\"publicationDate\", y=y_[i], ci=ci_, ax=axes[1,i])\n",
    "    temp.set_xlabel(\"Publication year\")\n",
    "    temp.set_ylabel(y_y[i])\n",
    "    temp.set_title(y_title[i])\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
